Progress in the treatment and early detection of cancer has resulted in the hope of extended survival and even cure for many patients. Interpreting survival patterns from studies with long-term survival prospects can be confounded by the fact that patients will be at high risk of death from natural causes unrelated to their disease. Thus, even in "curable" disease, one would never see a complete non-zero risk of death from natural causes. Competing risk methodologies provide a way to adjust for such phenomena. Although the approach has the potential to increase a study's power to detect clinical trials, natural history and epidemiological studies in cancer. The primary reason is that there are a number of different methods available, but few expository articles explaining when a competing risk analysis in likely to be helpful, and which method to use. In addition, user-friendly and appropriate software to perform specialized competing risks analyses are unavailable. The general aim of this proposal will be to review recent developments in statistical methods for the analysis of competing risks data in cancer studies with incomplete (censored) survival or failure time data, to illustrate their application using data from several clinical trial, and to provide clear recommendations and guidelines for when the use of competing risk methodologies will be beneficial. Breast cancer, lymphoma, and other diseases with high cure rates and long expected median survivals will be the primary emphasis.